Regrowth of bacterial pathogen indicators in electro- dewatered biosolids
Bibliographic record
Abstract
Land application of biosolids from activated sludge wastewater treatment plant (WWTP) is an attractive disposal solution. However, to be land applied in the US and Canada, biosolids need to meet specific pathogen loads regulations at the time of land application (i.e., not immediately after treatment). This study examined bacterial regrowth potential after electro-dewatering of biosolids. During a 8-min typical electro-dewatering treatment performed in the laboratory or a control heat treatment, Escherichia coli and fecal coliform (FC) counts were reduced to below the detection limit. After treatment, the extent of E. coli and FC regrowth was assessed by incubating the biosolids in aerobic or anaerobic conditions. After aerobic incubation, FC and E. coli counts stabilized between 107-109 MPN/g-dry solids in all biosolids samples irrespective of treatments (electro-dewatering, heat-treatment and no treatment) despite different levels of pH and dryness. Total aerobic counts also stabilized between 107-109 CFU/g-dry solids after four days of incubation. Finally, stable FC and E. coli counts at the end of incubation of samples prepared by belt filter press during winter were 1 log lower than in samples prepared by centrifuge press during the summer. Although similar trends about the effects of dewatering processes were reported in the literature, it is not be possible to conclude because seasonal factors are confounded with process factors. After anaerobic incubation, E. coli and FC counts in electro-dewatered biosolids stabilized 1 log lower than their respective counts in heat-treated or not treated biosolids (107-108 vs 108-109 MPN/g-dry solids, respectively). This difference was not changed when electro-dewatering filtrate was added back into the electro-dewatered biosolids. At WWTP, biosolids are typically stored as large piles. It is therefore likely that a major portion of piles is anaerobic, and that storage (hence regrowth) takes place under anaerobic condition. This study suggests that the electro-dewatered biosolids would exhibit a lower level of regrowth than other biosolids. Microbial counts observed in this study would not allow land application of these biosolids after 7-day storage because the lowest counts observed for electro-dewatered biosolids under anaerobic conditions were just above the requirements for the US-EPA Class B (FC counts above106 MPN/g-dry solids). However, these results suggest that this technology could be improved such that biosolids could meet land application regulations even after extended storage periods.
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How this classification was reachedexpand
Full frame distilled prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. Learned from the 10,348 direct Codex labels and 10,348 direct Gemma labels. Candidate is the union of thresholded teacher heads; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels or direct frontier model labels.
Codex and Gemma teacher scores by category
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.000 | 0.000 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.000 | 0.000 |
Machine scores (provisional)
The two teacher heads of the student model, read on this work. A score orders the frame for review; it never asserts a category, and the validation status ships verbatim with every row.
Baseline scores from an immature model (maturity gate not passed, 7 training rounds). Scores rank; they never assert a category.
score_only:v0-immature-baseline · verbatim from the scoring run: score_only means the number may rank works, and no category label ships from itClassification
machine, unvalidatedMachine predicted; a candidate call from one teacher head, not a consensus.
How this classification was reached, model by model and score by score, is at the end of the page under "How this classification was reached".